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{{Box|width=45%|float=right|font-size=14px|[[Neural Nets|Machine Learning Tutorial]]| | {{Box|width=45%|float=right|font-size=14px|[[Neural Nets|Machine Learning Tutorial]]| | ||
[[File:Machine learning tutorial.png| | [[File:Machine learning tutorial.png|340px]]{{Clear}} | ||
This project aims to provide annotated sets of [[Molecular Pathways|molecular pathways]] involved in neural plasticity underlying learning and memory systems. In general, biological pathways display the series of interactions among molecules resulting in functional changes within cells and neural networks. Currently there are large scale projects dedicated to amassing pathway evidence via high-throughput methods. The goal is to translate this unwieldy biopathway data from several [http://www.genome.jp/kegg/ empirical databases] into visually digestible material, by [[Molecular Pathways|characterizing]] the features of molecular cascades most sensitive to an ''event of interest'' (e.g. fear conditioning or amphetamine addiction). | This project aims to provide annotated sets of [[Molecular Pathways|molecular pathways]] involved in neural plasticity underlying learning and memory systems. In general, biological pathways display the series of interactions among molecules resulting in functional changes within cells and neural networks. Currently there are large scale projects dedicated to amassing pathway evidence via high-throughput methods. The goal is to translate this unwieldy biopathway data from several [http://www.genome.jp/kegg/ empirical databases] into visually digestible material, by [[Molecular Pathways|characterizing]] the features of molecular cascades most sensitive to an ''event of interest'' (e.g. fear conditioning or amphetamine addiction). |
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